genomics in medicine

GENOMICS IN

MEDICINEThe Future of Healthcare

Goal of Genomic Medicine

Identify genetic variation that causes or contributes to

disease (diagnostic), informs treatment options or patient

care (therapeutic/prognostic), or provides other useful

clinical information

Research Drives Innovation in Healthcare

Healthcare

Research

Innovation

Human Genome Project 1st Draft

Personalized Medicine: Expectations and Reality

Primary Clinical Applications

• Severe childhood genetic disorders

• Clinical Exome or Targeted Disease Panel

• Cheaper than 4 or 5 sequential gene tests

• Cystic Fibrosis Testing

• Oncology

• Classification

• Treatment Guidance

• Infectious disease

• Epidemiology/Outbreak monitoring

• Strain discrimination

What Drives Genomic Innovation in Medicine?

Cost

Knowledge Utility

The Players

Total Cost of Sequencing

• Whole Genome:

• Approximately $5000 - $10,000

• Technically $1000 Genome is “here” with Illumina 10X

Total Cost of Sequencing

• Whole Genome:

• Approximately $5000 - $10,000

• Technically $1000 Genome is “here” with Illumina 10X

• Analysis cost >> Sequencing Cost

Total Cost of Sequencing

• Whole Genome:

• Approximately $5000 - $10,000

• Technically $1000 Genome is “here” with Illumina 10X

• Analysis cost >> Sequencing Cost

• But, do we need the whole genome?

Composition of the Human Genome

Exome Sequencing

Targeted Sequencing Panels

What Drives Genomic Innovation in Medicine?

Cost

Knowledge Utility

Bioinformatics Roles

• Support/Maintain Computational Infrastructure

•Raw data -> Genome/Exome

• Identify genetic variation

•Annotate genetic variation

•Quality Control

•Report to Stake Holders (Clinicians, Fellow

Scientists)

Typical Bioinformatics Workflow

QC of Raw Data

Map to Reference

QC

Find Variants

QC

Annotate

Filter

It Sounds simple but…

• For every stage there are multiple programs available and

published in the literature

It Sounds simple but…

• For every stage there are multiple programs available and

published in the literature

• For every program there are a wide-variety of parameter

values and options. Defaults often “good enough” but

not always

It Sounds simple but…

• For every stage there are multiple programs available and

published in the literature

• For every program there are a wide-variety of parameter

values and options. Defaults often “good enough” but

not always

• Best combinations of programs and options not well

understood

It Sounds simple but…

• For every stage there are multiple programs available and

published in the literature

• For every program there are a wide-variety of parameter

values and options. Defaults often “good enough” but

not always

• Best combinations of programs and options not well

understood

• Protocols changing rapidly as new technologies and

methods developed

Clinical Bioinformatics

Validate, validate, validate!

Typical Bioinformatics Workflow

QC of Raw Data

Map to Reference

QC

Find Variants

QC

Annotate

Filter

Clinical Genomics: Identify Clinically Relevant

Genetic Variation

Discovering Disease-Causing Genetic Variants

4 million genetic variants

2 million associated with protein-coding genes

10,000 possibly of disease

causing type

1500 <1% frequency in population

Clinically

Relevant Genetic

Variants

If a problem cannot be

solved, enlarge it.

--Dwight D. Eisenhower

Supreme Commander Allied Forces:

Second World War

34th President of the USA

4 million genetic variants

2 million associated with protein-coding genes

10,000 possibly of disease

causing type

1500 <1% frequency in population

Knowledge Required

Variant

Gene

Population

Frequency

Pathways

Functions

Tissues

Variant

Type

Impact on

Protein

Populations are Important

2001 – Present: 14 years of Knowledge Building

Exome Variant Server

Exome Aggregation Consortium

2001 – Present: 14 years of Knowledge Building

2001 – Present: 14 years of Knowledge Building

Building Knowledge Take-Away

•Clinical utility relies on:

• Knowledge of background variation from well

sampled populations

• Knowledge of function of as much genomic

sequence as possible

• Well defined workflows

• Knowledge of sources of error

Variant Annotation Pipeline Example

Variant Annotation Pipeline Example

Genetic Variant Reporting

Genetic Variant Reporting

Genetic Variation Reporting

Genetic Variation Reporting

Genetic Variation Reporting

Potential Pitfalls with Annotation Sources

• Databases often overlap and agree, but there may be

disagreements

• Source of information: Predicted versus experimental

• Incorrect and out-of-date information

• Large-scale un-validated versus manually curated datasets

What Drives Genomic Innovation in Medicine?

Cost

Knowledge Utility

Genomic Medicine: In the Clinic

• Rapid diagnosis of genetic disease in NICU cases

• Quicker and cheaper than sequential genetic testing (traditional

method)

• 50 hour diagnosis

Genomic Medicine: In the Clinic

Genomic Medicine: In the Clinic

Genomic Medicine: In the Clinic

Genomic Medicine: In the Clinic

Genomic Medicine: In the Clinic

Types of Next-Generation Sequencing

Experiments

•DNA-Seq

•RNA-Seq

•Methyl-Seq

•ChIP-Seq

•CLIP-Seq

The Missing Pieces?

The Missing Pieces?

The Missing Pieces?

The Missing Pieces?

The Missing Pieces?

The Missing Pieces?

The Missing Pieces?

The Missing Pieces?

Exon 1 Intron 1 Exon 2Reference

Patient

StartTAA

StopmRNA coding for protein

Exon 1 Intron 1 Exon 2

TAC

TyrSplice Site Loss

Missense/Frameshift Stop Gain

Where Are We Going?

Where Are We Going?

Do whole genome anyway, use bioinformatics to filter

down to reportable/actionable information

4 million genetic variants

2 million associated with protein-coding genes

10,000 possibly of disease

causing type

1500 <1% frequency in population

Clinically

Relevant Genetic

Variants

Where Are We Going?

Direct-to-Consumer

New Technologies: Oxford Nanopore

Summary of Key Points

• Clinical application possible when cost and applicable

knowledge reach critical point

• Personalized genomic medicine is here already

• The genome alone isn’t enough

• Large population surveys of healthy individuals

• Sample from diverse human populations globally

• Large-scale surveys of genes, genetic elements, and their

functions

• Data, data, and more data required